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雷达卡
摘要翻译：
由于无线传感器网络和物联网技术在传输、计算和能量资源等方面的限制，使得利用无线传感器网络和物联网技术进行大规模数据采集成为挑战。基于压缩感知的压缩数据采集已经被证明是解决这一问题的一个很好的方法。现有的设计利用了由特定感知方式收集的数据之间的时空相关性。然而，许多应用，如环境监测，涉及收集内在相关的异构数据。在这项研究中，我们建议在从压缩测量中恢复数据时，利用来自多个异构信号的相关性。为此，我们提出了一种新的恢复算法--基于信念传播原理--利用多个异构信号中的相关信息。为了有效地捕捉不同传感器数据之间的统计相关性，该算法使用了copula函数的统计模型。对异构空气污染传感器测量的实验表明，与使用经典压缩感知、附带信息压缩感知和分布式压缩感知的现有压缩数据收集和恢复方案相比，本文提出的设计提供了显着的性能改进。
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英文标题：
《Heterogeneous Networked Data Recovery from Compressive Measurements
Using a Copula Prior》
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作者：
Nikos Deligiannis, Jo\~ao F. C. Mota, Evangelos Zimos, Miguel R. D.
Rodrigues
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最新提交年份：
2017
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
Large-scale data collection by means of wireless sensor network and internet-of-things technology poses various challenges in view of the limitations in transmission, computation, and energy resources of the associated wireless devices. Compressive data gathering based on compressed sensing has been proven a well-suited solution to the problem. Existing designs exploit the spatiotemporal correlations among data collected by a specific sensing modality. However, many applications, such as environmental monitoring, involve collecting heterogeneous data that are intrinsically correlated. In this study, we propose to leverage the correlation from multiple heterogeneous signals when recovering the data from compressive measurements. To this end, we propose a novel recovery algorithm---built upon belief-propagation principles---that leverages correlated information from multiple heterogeneous signals. To efficiently capture the statistical dependencies among diverse sensor data, the proposed algorithm uses the statistical model of copula functions. Experiments with heterogeneous air-pollution sensor measurements show that the proposed design provides significant performance improvements against state-of-the-art compressive data gathering and recovery schemes that use classical compressed sensing, compressed sensing with side information, and distributed compressed sensing.
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PDF链接：
https://arxiv.org/pdf/1709.07744
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